The present invention is concerned with an entropy coding concept for coding video data.
Many video codecs are known in the art. Generally, these codecs reduce the amount of data necessitated in order to represent the video content, i.e. they compress the data. In the context of video coding, it is known that the compression of the video data is advantageously achieved by sequentially applying different coding techniques: motion-compensated prediction is used in order to predict the picture content. The motion vectors determined in motion-compensated prediction as well as the prediction residuum are subject to lossless entropy coding. In order to further reduce the amount of data, the motion vectors themselves are subject to prediction so that merely motion vector differences representing the motion vector prediction residuum, have to be entropy encoded. In H.264, for example, the just-outlined procedure is applied in order to transmit the information on motion vector differences. In particular, the motion vector differences are binarized into bin strings corresponding to a combination of a truncated unary code and, from a certain cutoff value on, an exponential Golomb code. While the bins of the exponential Golomb code are easily coded using an equi-probability bypass mode with fixed probability of 0.5, several contexts are provided for the first bins. The cutoff value is chosen to be nine. Accordingly, a high amount of contexts is provided for coding the motion vector differences.
Providing a high number of contexts, however, not only increases coding complexity, but may also negatively affect the coding efficiency: if a context is visited too rarely, the probability adaptation, i.e. the adaptation of the probability estimation associated with the respective context during the cause of entropy coding, fails to perform effectively. Accordingly, the probability estimations applied inappropriately estimate the actual symbol statistics. Moreover, if for a certain bin of the binarization, several contexts are provided, the selection there among may necessitate the inspection of neighboring bins/syntax element values whose necessity may hamper the execution of the decoding process. On the other hand, if the number of contexts is provided too low, bins of highly varying actual symbol statistics are grouped together within one context and accordingly, the probability estimation associated with that context fails to effectively encode the bins associated therewith.
There is an ongoing need to further increase the coding efficiency of entropy coding of motion vector differences.